Co-pyrolysis of solid oily waste for efficient waste reduction and energy recovery in marine oil spill response
Bibliographic record
Abstract
Despite substantial advances in cleaning up marine spills, solid oily waste generation during a spill is an inevitable problem that could easily exceed the capacity of locally available waste management facilities. This thesis developed a pyrolysis system to cope with multiple oily solid waste streams simultaneously. The treatment of contaminated sorbent through co-pyrolysis with Personal Protective Equipment (PPE) that are two types of solid oily waste in marine oil spill response was carried out in a heating mantle system over temperature and heating rate ranges of 350 to 550 °C and 5 to 20 °C/min, respectively. Response surface method was applied to evaluate the main and interaction effects of three experimental factors (contaminated sorbent ratio in feedstock, heating rate, and temperature) on pyrolysis oil and gas yields. It was found that gas yield decreased while the oil yield increased with raising the contaminated sorbent percentage in feedstock. Total weight loss as high as 99.6% (liquid; 96.8%, gas; 2.8%, and char; 0.4%) and 84.4% (liquid; 77.4%, gas; 7.0%, and char; 15.6%) was achieved at the contaminated sorbent ratios of 100% and 80%, respectively. The oil yield value of 98.1% under optimal experimental conditions (temperature; 460 °C, heating rate; 5 °C /min, and contaminated sorbent ratio; 99%) with 1% error compared to the predicted value of 97.1% validated the model. The oil obtained through co-pyrolysis at a contaminated sorbent ratio of 75% exhibited a Higher Heating Value (HHV) of 43.67 MJ/Kg, thereby suggesting its potential as an energy source. As a result, multiple types of solid oily waste can be treated through the co-pyrolysis process to produce energy-rich products, reduce the amount of waste, recover spilled hydrocarbons, and improve energy recovery.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".